class_docstr: Categorical parameter, choosing one of the provided choice options as a value, with continuous transitions.
  By default, this is ordered, and most algorithms except discrete OnePlusOne algorithms will consider it as ordered.
  The choices can be Parameters, in which case there value will be returned instead.
  The chosen parameter is drawn using transitions between current choice and the next/previous ones.
  
  Parameters
  ----------
  choices: list or int
      a list of possible values or Parameters for the variable (or an integer as a shortcut for range(num))
  transitions: np.ndarray or Array
      the transition weights. During transition, the direction (forward or backward will be drawn with
      equal probabilities), then the transitions weights are normalized through softmax, the 1st value gives
      the probability to remain in the same state, the second to move one step (backward or forward) and so on.
  ordered: bool
      if False, changes the default behavior to be unordered and sampled uniformly when setting the data to a
      normalized and centered Gaussian (used in DiscreteOnePlusOne only)
  
  Note
  ----
  - the "mutate" method only mutates the weights and the chosen Parameter (if it is not constant),
    leaving others untouched
  - in order to support export to standardized space, the index is encoded as a scalar. A normal distribution N(O,1)
    on this scalar yields a uniform choice of index. This may come to evolve for simplicity's sake.
  - currently, transitions are computed through softmax, this may evolve since this is somehow impractical
code: def _internal_set_standardized_data(
      self: T, data: np.ndarray, reference: T
  ) -> None:
      ref = reference if self._ref is None else self._ref
      super()._internal_set_standardized_data(data, ref)  # type: ignore
      # force to bin
      super()._layered_set_value(super()._layered_get_value())
  
imports: import numpy
  import nevergrad.common.typing
  from .None import discretization
  from .None import core
  from .None import container
  from .None import _datalayers
  from .data import Array
label_desc: Use image node_id 2 for calling the TransitionChoice obj's underlying member method code with example usage: obj._internal_set_standardized_data(data, reference) without return types